An Empirical Analysis of Cost Efficiency in Non‐profit and Public Nursing Homes

Abstract *** :  This paper examines the issue of cost‐efficiency in Switzerland's nursing homes, an issue of concern to policy makers because of the rapid growth of elderly care expenditure and the aging of the population. The fact that nursing homes in Switzerland exist in different institutional forms, private for‐profit, government and non‐profit status, raises the issue of their relative cost efficiency. A panel data of 17 public and 19 nonprofit nursing homes operating over the 9‐year period from 1993 to 2001, in one of the 26 Swiss cantons, Ticino, is studied. Ticino's nursing homes are heavily regulated and monitored by the canton's authorities. However, given that in public firms there are more bureaucratic constraints and agency problems, one can expect a relatively low level of cost‐efficiency. In this paper the effect of institutional form on efficiency is studied using a translog stochastic cost‐frontier model. Several specifications are used to study the robustness of the results. The results suggest that the institutional form influences the efficiency of the studied nursing homes in that non‐profit foundations are likely to be more cost‐efficient than the nursing homes operated by government administration. The results also suggest that a great majority of the nursing homes in the sample do not fully benefit from scale economies. This implies that efficiency gains can be obtained with larger capacities or joint operations .     

The evolution of value added in assembly operations: the case of China and Mexico

China and Mexico embarked, around the early 1980s, on the process of liberalization of trade and FDI and established some export oriented industries through assembly operations with the help of FDI. Both countries had the same objective of increasing value added in export processing industries. The authors examine the comparative achievements of their common objectives in the light of the export oriented strategy hypothesis. Applying the indicators of Revealed Comparative Advantage to exports and imports, the authors show that while there are some similarities in the performance of the two countries, there are also striking differences. Both have managed to increase manufactured exports, particularly in IT products, significantly during 1990s. Both, but particularly China, have developed comparative advantage in export and production of many industries, which had been initiated through import substitution. However, Mexico has achieved little, compared with China, in increasing value added in exports and in developing comparative advantage in production in assembly operations. Further, unlike China, since the early years of the 2010 decade it has not been able to sustain its rapid export growth.     

Empirical likelihood-based inference for the generalized entropy class of inequality measures

We propose an empirical likelihood-based method of inference for comparing inequality between two populations. A series of Monte Carlo experiments are used to assess our method’s finite sample performance. We illustrate our approach using some Canadian household income data.     

Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models

We present a neural network-based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface. The framework is consistently applicable throughout a range of volatility models—including second-generation stochastic volatility models and the rough volatility family—and a range of derivative contracts. Neural networks in this work are used in an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. The form in which information from available data is extracted and used influences network performance: The grid-based algorithm used for calibration is inspired by representing the implied volatility and option prices as a collection of pixels. We highlight how this perspective opens new horizons for quantitative modelling. The calibration bottleneck posed by a slow pricing of derivative contracts is lifted, and stochastic volatility models (classical and rough) can be handled in great generality as the framework also allows taking the forward variance curve as an input. We demonstrate the calibration performance both on simulated and historical data, on different derivative contracts and on a number of example models of increasing complexity, and also showcase some of the potentials of this approach towards model recognition. The algorithm and examples are provided in the Github repository GitHub: NN-StochVol-Calibrations.     

Income mobility and the Arab spring: the case of Egypt and Jordan

Arab Spring has swept Middle East and North Africa. Although the reasons behind this revolutionary albeit mostly unfruitful social phenomenon is unknown, economic reasons such as income inequality and lack of income mobility have been mentioned. Using an approach that is free of measurement error and attrition biases, this study estimates income mobility in a country that went through Arab Spring, Egypt, and compares that with a similar country that did not experience it, Jordan. The results show that unconditional and conditional income mobility were substantially smaller in Egypt than Jordan. This has implications for the policy-makers not just in the Middle East but the world.     

Regulation and Measuring Cost-Efficiency with Panel Data Models: Application to Electricity Distribution Utilities

This paper examines the performance of panel data models in measuring cost-efficiency of electricity distribution utilities. Different cost frontier models are applied to a sample of 59 utilities operating in Switzerland from 1988 to 1996. The estimated coefficients and inefficiency scores are compared across different specifications. The results indicate that while the average inefficiency is not sensitive to the econometric specification, the efficiency ranking varies significantly across models. The reasonably low out-of-sample prediction errors suggest that panel data models can be used as a prediction instrument in order to narrow the information gap between the regulator and regulated companies.     

Are free trade agreements good for the environment? A panel data analysis

This study empirically examines the relationship between free trade agreements (FTAs) and greenhouse gas (GHG) emissions. For this purpose, we use three different FTAs: the Southern Common Market, the North American Free Trade Agreement, and the Australia–United States Free Trade Agreement. These FTAs are between developing, both developing and developed, and only developed countries, respectively. Panel unit root, panel cointegration, and fully modified ordinary least squares estimations are employed to examine the long‐run relationship between GHG emissions and trade liberalization. The results indicate that the environmental effects of FTAs depend on the different agreement types. When FTAs are between only developed or developing countries, overall there is no environmental damage, and these types of FTAs can be beneficial for the environmental quality in the long run. However, when developing and developed countries are in a trade agreement, overall environmental quality decreases due to increased GHG emissions.     

Do Tighter Loan Covenants Signal Improved Future Corporate Results? The Case of Performance Pricing Covenants

Covenants in corporate bonds and loan agreements mitigate agency conflicts between borrowers and lenders and may provide a signal of borrower quality to help resolve information asymmetry. Performance pricing covenants in bank loans specify automatic adjustments to loan spreads based on borrowers’ subsequent performance. Our covenant signaling framework views interest‐decreasing performance pricing as a tight covenant associated with borrowers’ private information on improved future performance accompanied by reduced credit risk. This positive signal is associated with larger positive loan announcement returns and greater improvements in future borrower performance. Further, in addition to signaling value, we find that the spread impact of this class of covenant also depends on its option value and reduction in transaction costs.     

Ex-post favoring ranks: a fairness notion for the random assignment problem

We introduce two notions of ex-post fairness, namely ex-post favoring ranks (EFR) and robust ex-post favoring ranks, which consider whether objects are received by those agents who have the highest rank for them. We examine their compatibility with standard properties of random assignments and state some impossibility theorems. We also propose and formalize a revised version of the Boston mechanism and prove that it provides an EFR random assignment.